Merge pull request #2 from SimonWaldherr/patch-1

link to new, recommended DOI resolver in README.md

Author: johanneskoester

report/50-highest-genes.rst

@@ -12,4 +12,4 @@ with expected biology. For example, a top set containing many spike-in
 transcripts suggests that too much spike-in RNA was added during library
 preparation, while the absence of ribosomal proteins and/or the presence of
 their pseudogenes are indicative of suboptimal alignment.
-See `Lun et al. (2016) <http://dx.doi.org/10.12688/f1000research.9501.2>`_.
+See `Lun et al. (2016) <https://doi.org/10.12688/f1000research.9501.2>`_.

report/cycle-scores.rst

@@ -1,3 +1,3 @@
-Cells are classified into cycle phases using the method of `Scialdone et al. (2015) <http://dx.doi.org/10.1016/j.ymeth.2015.06.021>`_.
+Cells are classified into cycle phases using the method of `Scialdone et al. (2015) <https://doi.org/10.1016/j.ymeth.2015.06.021>`_.
 This plot shows the scores for G1 and G2/M, each point represents a cell.
 Points are color coded by {{ snakemake.wildcards.condition }}.

report/explained-variance.rst

@@ -4,4 +4,4 @@ For each feature, the percentage of the variance of the normalized
 log-expression values across cells that is explained by each covariate is
 calculated. Each curve corresponds to one factor and represents the distribution
 of percentages across all genes. See `Lun et al. (2016)
-<http://dx.doi.org/10.12688/f1000research.9501.2>`_.
+<https://doi.org/10.12688/f1000research.9501.2>`_.

report/filtering.rst

@@ -1,4 +1,4 @@
-Cells were filtered as suggested by `Lun et al. (2016) <http://dx.doi.org/10.12688/f1000research.9501.2>`_ by removing by removing outliers regarding
+Cells were filtered as suggested by `Lun et al. (2016) <https://doi.org/10.12688/f1000research.9501.2>`_ by removing by removing outliers regarding
 
 * library size,
 * number of expressed features,

report/hvg-clusters.rst

@@ -2,4 +2,4 @@ Larger sets of correlated features were assembled by treating features as nodes
 in a graph and each pair of features with significantly large
 (:math:`FDR \geq {{snakemake.params.fdr}}`) correlations as an edge.
 Clusters in this graph represent a set of correlated features.
-See `Lun et al. (2016) <http://dx.doi.org/10.12688/f1000research.9501.2>`_.
+See `Lun et al. (2016) <https://doi.org/10.12688/f1000research.9501.2>`_.

report/hvg-corr-heatmap.rst

@@ -2,4 +2,4 @@ Heatmap of mean-centred normalized log-expression values for significantly
 (:math:`FDR \geq {{snakemake.params.fdr}}`) correlated HVGs.
 Dendrograms were formed by hierarchical clustering on the Euclidean distances
 between features (rows) or cells (columns).
-See `Lun et al. (2016) <http://dx.doi.org/10.12688/f1000research.9501.2>`_.
+See `Lun et al. (2016) <https://doi.org/10.12688/f1000research.9501.2>`_.

report/hvg-corr-pca.rst

@@ -4,4 +4,4 @@ where each point represents a cell.
 First, second, and third components are shown, along with the percentage of variance
 explained. Bars represent the coordinates of the cells on each axis.
 Cells were colored by {{ snakemake.wildcards.covariate }}.
-See `Lun et al. (2016) <http://dx.doi.org/10.12688/f1000research.9501.2>`_.
+See `Lun et al. (2016) <https://doi.org/10.12688/f1000research.9501.2>`_.

report/hvg-correlations.rst

@@ -1,4 +1,4 @@
 Table of pairs of correlated HVGs.
 
 Correlations between genes are quantified by computing Spearman's rho, which accommodates non-linear relationships in the expression values.
-See `Lun et al. (2016) <http://dx.doi.org/10.12688/f1000research.9501.2>`_.
+See `Lun et al. (2016) <https://doi.org/10.12688/f1000research.9501.2>`_.

report/hvg.rst

@@ -7,7 +7,7 @@ technical components. HVGs are then identified as those genes with the largest
 biological components. This avoids prioritizing genes that are highly variable
 due to technical factors such as sampling noise during RNA capture and library
 preparation. See
-`Lun et al. (2016) <http://dx.doi.org/10.12688/f1000research.9501.2>`_.
+`Lun et al. (2016) <https://doi.org/10.12688/f1000research.9501.2>`_.
 
 {% if snakemake.params.use_spikes -%}
 Variance was estimated by fitting a mean-variance trend to spike-in transcript

report/mean-vs-variance.rst

@@ -2,4 +2,4 @@ Variance of normalized log-expression values for each feature, plotted against
 the mean log-expression. The blue line represents the mean-dependent trend
 fitted to the variances of the endogenous genes. Variance estimates for spike-in
 transcripts are highlighted in red.
-See `Lun et al. (2016) <http://dx.doi.org/10.12688/f1000research.9501.2>`_.
+See `Lun et al. (2016) <https://doi.org/10.12688/f1000research.9501.2>`_.